Search Results (93)

To address the node voltage fluctuation and over-limit caused by the high penetration of distributed photovoltaic (PV) generation connected to distribution networks, this paper proposes a network partition-based optimal reactive power allocation and sizing method in the active distribution network (ADN). A network index incorporating network partition and critical node identification is introduced to obtain the optimal location for the reactive power compensation. A singular value entropy-based adaptive spectral clustering algorithm is applied to obtain the initial zones and obtain the critical nodes of each zone on the basis of the proposed network indexes. This method avoids the unreasonable scheme and enhances the efficiency and clarity of partitioning. The improved decimal coding method is proposed to improve the efficiency of the proposed method. A case study on the IEEE 33-node distribution system is carried out to verify the feasibility and effectiveness of the proposed method. The results show that compared with the conventional methods, the proposed method can effectively reduce voltage variations and control the voltage within the safe limit. Full article

The high-proportion integration of renewable energy sources, represented by wind power and photovoltaics, into active distribution networks (ADNs) can effectively alleviate the pressure associated with advancing China’s dual-carbon goals. However, the high uncertainty in renewable energy output leads to increased system voltage fluctuations and localized voltage violations, posing safety challenges. Consequently, research on optimal dispatch for ADNs with a high penetration of renewable energy has become a current focal point. This paper provides a comprehensive review of research in this domain over the past decade. Initially, it analyzes the voltage impact patterns and control principles in distribution networks under varying levels of renewable energy penetration. Subsequently, it introduces optimization dispatch models for ADNs that focus on three key objectives: safety, economy, and low carbon emissions. Furthermore, addressing the challenge of solving non-convex and nonlinear models, the paper highlights model reformulation strategies such as semidefinite relaxation, second-order cone relaxation, and convex inner approximation methods, along with summarizing relevant intelligent solution algorithms. Additionally, in response to the high uncertainty of renewable energy output, it reviews stochastic optimization dispatch strategies for ADNs, encompassing single-stage, two-stage, and multi-stage approaches. Meanwhile, given the promising prospects of large-scale deep reinforcement learning models in the power sector, their applications in ADN optimization dispatch are also reviewed. Finally, the paper outlines potential future research directions for ADN optimization dispatch. Full article

Under the guidance of carbon peaking and carbon neutrality goals, the power industry is transitioning toward environmentally friendly practices. With the increasing integration of intermittent renewable energy sources (RES) and the enhanced self-regulation capabilities of grids, traditional distribution networks (DNs) are transitioning into active distribution networks (ADNs). To fully exploit the synergistic optimization potential of the “source-grid-load-storage” system in electricity-carbon coupling scenarios, leverage user-side flexibility resources, and facilitate low-carbon DN development, this paper proposes a low-carbon optimal scheduling strategy for ADN incorporating demand response (DR) priority. Building upon a bi-directional feedback mechanism between carbon potential and load, a two-layer distributed robust scheduling model for DN is introduced, which is solved through hierarchical iteration using column and constraint generation (C&CG) algorithm. Case study demonstrates that the model proposed in this paper can effectively measure the priority of demand response for different loads. Under the proposed strategy, the photovoltaic (PV) consumption rate reaches 99.76%. Demand response costs were reduced by 6.57%, and system carbon emissions were further reduced by 8.93%. While accounting for PV uncertainty, it balances the economic efficiency and robustness of DN, thereby effectively improving system operational safety and reliability, and promoting the smooth evolution of DN toward a low-carbon and efficient operational mode. Full article

Abnormal operations, such as faults occurring in an electrical power system (EPS), disrupt its balanced operation, posing potential hazards to human lives and the system’s equipment. Effective monitoring, control, protection, and coordination are essential to mitigate these risks. The complexity of these processes is further compounded by the presence of intermittent distributed energy resources (DERs) in active distribution networks (ADNs) with bidirectional power flow, which introduces a fast-changing dynamic aspect to the system. The deployment of phasor measurement units (PMUs) within the EPS as highly responsive equipment can play a pivotal role in addressing these challenges, enhancing the system’s resilience and reliability. However, synchrophasor measurement-based studies and analyses of power system phenomena may be hindered by the absence of PMU blocks in certain simulation tools, such as PSCAD, or by the existing PMU block in Matlab/Simulink R2021b, which exhibit technical limitations. These limitations include providing only the positive sequence component of the measurements and lacking information about individual phases, rendering them unsuitable for certain measurements, including unbalanced and non-symmetrical fault operations. This study proposes a new reliable PMU model in Matlab and tests it under normal and abnormal conditions, applying real-time simulation and controller-hardware-in-the-loop (CHIL) techniques. Full article

This paper proposes a Distributed Cooperative Algorithm (DCA) that solves the windup problem caused by the saturation of the Distributed Energy Resource (DER) PI-based control unit. If the reference reactive current output by the PI exceeds the maximum reactive power capacity of the DER, the control unit saturates, preventing the optimal voltage regulation at the connection node of the Active Distribution Network (ADN). Instead of relying on a centralized solution, we proposed a cooperative approach in which each DER’s control unit takes part in the DCA. If a control unit saturates, the voltage regulation error is not null, and the algorithm is activated to assign a share of this error to all DERs’ control units according to a weighted average principle. Subsequently, the algorithm determines the control unit’s new value of the voltage setpoint, desaturating the DER and enhancing the voltage profile. The proposed DCA is independent of the design of the control unit, does not require parameter tuning, exchanges only the regulation error at a low sampling rate, handles multiple saturations, and has limited communication requirements. The effectiveness of the proposed DCA is validated through numerical simulations of an ADN composed of two IEEE 13-bus Test Feeders. Full article

The integration of battery energy storage systems (BESSs) within active distribution networks (ADNs) entails optimized day-ahead charge/discharge scheduling to achieve effective peak shaving.The primary objective is to reduce peak demand and mitigate power deviations caused by intermittent photovoltaic (PV) output. Quasi-static time-series (QSTS) co-simulations for determining optimal heuristic solutions at each time interval are computationally intensive, particularly for large-scale systems. To address this, a two-stage intelligent BESS scheduling approach implemented in a MATLAB–OpenDSS environment with parallel processing is proposed in this paper. In the first stage, a rule-based decision tree generates initial charge/discharge setpoints for community BESS units. These setpoints are refined in the second stage using an optimization algorithm aimed at minimizing community net load power deviations and reducing peak demand. By assigning each ADN community to a dedicated CPU core, the proposed approach utilizes parallel processing to significantly reduce the execution time. Performance evaluations on an IEEE 8500-node test feeder demonstrate that the approach enhances peak shaving while reducing QSTS co-simulation execution time, utility peak demand, distribution network losses, and point of interconnection (POI) nodal voltage deviations. In addition, the use of smart inverter functions improves BESS operations by mitigating voltage violations and active power curtailment, thereby increasing the amount of energy shaved during peak demand periods. Full article

In distribution networks, energy storage serves as a crucial means to mitigate power fluctuations from renewable energy sources. However, due to its high cost, energy storage remains a resource whose large-scale adoption in power systems faces significant challenges. In recent years, the emergence of shared energy storage business models has provided new opportunities for the efficient operation of multi-distribution networks. Nevertheless, distribution network operators and shared energy storage operators belong to different stakeholders, and traditional centralized scheduling strategies suffer from issues such as privacy leakage and overly conservative decision-making. To address these challenges, this paper proposes a Nash bargaining game-based optimal energy management and trading strategy for multi-distribution networks with shared energy storage. First, we establish optimal scheduling models for active distribution networks (ADNs) and shared energy storage operators, respectively, and then develop a cooperative scheduling model aimed at maximizing collaborative benefits. The interactive variables—power exchange and electricity prices between distribution networks and shared energy storage operators—are iteratively solved using the Alternating Direction Method of Multipliers (ADMM). Finally, case studies based on modified IEEE-33 test systems validate the effectiveness and feasibility of the proposed method. The results demonstrate that the presented approach significantly outperforms conventional centralized optimization and distributed robust techniques, achieving a maximum improvement of 3.6% in renewable energy utilization efficiency and an 11.2% reduction in operational expenses. While maintaining computational performance on par with centralized methods, it effectively addresses data privacy concerns. Furthermore, the proposed strategy enables a substantial decrease in load curtailment, with reductions reaching as high as 63.7%. Full article

The increasing integration of distributed photovoltaics (PVs) has intensified voltage violations in active distribution networks (ADNs). Traditional centralized voltage regulation approaches face substantial challenges in terms of communication and computation. Distributed control methods can help mitigate these issues through distributed algorithms but struggle to track real-time fluctuations in PV generation. Local control offers fast voltage adjustments but lacks coordination among different PV units. This paper presents a hierarchical distributed and local voltage control strategy for PV clusters. First, the alternating direction method of multipliers (ADMM) algorithm is adopted to coordinate the reactive power outputs of PV inverters across clusters, providing reference values for local control. Then, in the local control phase, a Q-P control strategy is utilized to address real-time PV fluctuations. The flexibility of the local control strategy is enhanced using the lifted linear decision rule, enabling a rapid response to PV power fluctuations. Finally, the proposed strategy is tested on both the modified IEEE 33-node distribution system and a practical 53-node distribution system to evaluate its performance. The results demonstrate that the proposed method effectively mitigates voltage issues, reducing the average voltage deviation by 53.93% while improving flexibility and adaptability to real-time changes in PV output. Full article

With the proposal of carbon peaking and carbon neutrality goals, the proportion of distributed renewable energy generation in active distribution networks (ADNs) has been continuously increasing. While this has effectively reduced greenhouse gas emissions, it has also given rise to power quality issues such as excessive or insufficient voltage amplitudes. To effectively address this problem, this paper proposes a multi-resource coordinated dynamic reactive power–voltage coordination optimization method. Firstly, an improved Generative Convolutional Adversarial Network (GCAN) is used to generate typical wind and solar power output scenarios. Based on these generated typical scenarios, a voltage control model for ADNs is established with the objective of minimizing voltage fluctuations, fully exploiting the dynamic reactive power regulation resources within the ADN. In view of the non-convex and nonlinear characteristics of the model, an improved Gray Wolf Optimizer (GWO) algorithm is employed for model optimization and solution seeking. Finally, the effectiveness and feasibility of the proposed method are demonstrated through simulations using modified IEEE-33-bus and IEEE-69-bus test systems. Full article

In the context of rapid advancement of smart cities, a distribution network (DN) serving as the backbone of urban operations is a way to confront multifaceted challenges that demand innovative solutions. Central among these, it is imperative to optimize resource allocation and enhance the efficient utilization of diverse energy sources, with particular emphasis on seamless integration of renewable energy systems into existing infrastructure. At the same time, considering that the traditional power system’s “rigid”, instantaneous, dynamic, and balanced law of electricity, “source-load”, is difficult to adapt to the grid-connection of a high proportion of distributed generations (DGs), the collaborative interaction of multiple flexible controllable resources, like flexible loads, are able to supplement the power system with sufficient “flexibility” to effectively alleviate the uncertainty caused by intermittent fluctuations in new energy. Therefore, an active distribution network (ADN) intraday, reactive, power optimization-scheduling model is designed. The dynamic reactive power collaborative interaction model, considering the integration of DG, energy storage (ES), flexible loads, as well as reactive power compensators into the IEEE 33-node system, is constructed with the goals of reducing intraday network losses, keeping voltage deviations to a minimum throughout the day, and optimizing static voltage stability in an active distribution network. Simulation outcomes for an enhanced IEEE 33-node system show that coordinated operation of source–network–load–storage effectively reduces intraday active power loss, improves voltage regulation capability, and achieves secure and reliable operation under ADN. Therefore, it will contribute to the construction of future smart city power systems to a certain extent. Full article

Optimizing the operation of active distribution networks (ADNs) has become more challenging because of the uncertainty created by the high penetration level of distributed photovoltaic (PV). From the convex optimization perspective, this paper proposes a two-layer optimization model to simplify the solution of the ADN optimal operation problem. Firstly, to pick out the ADN “key” nodes, a “key” nodes selection approach that used improved K-means clustering algorithm and two indexes (integrated voltage sensitivity and reactive power-balance degree) is introduced. Then, a two-layer ADN optimization model is built using various time scales. The upper layer is a long-time-scale model with on-load tap-changer transformer (OLTC) and capacitor bank (CB), and the lower layer is a short-time-scale optimization model with PV inverters and distributed energy storages (ESs). To take into account the PV users’ interests, maximizing PV active power output is added to the objective. Afterwards, under the application of the second-order cone programming (SOCP) power-flow model, a linearization method of OLTC model and its tap change frequency constraints are proposed. The linear OLTC model, together with the linear models of the other equipment, constructs a mixed-integer second-order cone convex optimization (MISOCP) model. Finally, the effectiveness of the proposed method is verified by solving the IEEE33 node system using the CPLEX solver. Full article

Aiming at solving the problem of new energy and load uncertainty leading to a steep increase in the demand for flexible reserve resources by integrated energy service providers (IESPs), a coordinated and optimized scheduling method for multi-region integrated energy service providers considering flexible reserve resources is proposed. First, for the uncertainty of new energy and load, Latin hypercube sampling is used to generate scenarios, and the scenarios are reduced by a K-means clustering algorithm. Second, based on the interaction relationship between the active distribution network (ADN) and multi-region IESPs, a mixed game model of the ADN and IESP alliance is established. ADN guides IESPs to optimize their operation by setting prices for electricity and reserves, and IESPs fully tap their own flexible reserve resources according to the prices set by ADN and achieve power interoperability through the interaction of IESPs in multiple regions to synergistically cope with the uncertainties of new energy and load. Finally, the example results show that the model proposed in this paper is able to realize the allocation of flexibility resources in a wider range, reduce the reserve pressure on the superior grid, and improve the profitability of IESPs. Full article

Flexible resources (FRs) have significant potential in ensuring the dynamic balance between supply and demand as well as enhancing the security of active distribution networks (ADNs). However, determining the optimal FR capacity in an economically reasonable manner remains a challenging task. This paper addresses the lack of representativeness of wind turbine (WT) and photovoltaic (PV) power output scenarios in the planning stage by generating a basic set of joint WT-PV output scenarios using random sampling. Subsequently, a Wasserstein confidence set (WCS) is established based on data-driven technology to better represent the unknown distribution of the actual WT-PV joint fluctuations. This provides a more detailed description of the scenario set, enabling the precise quantification of the risk of resource allocation scenarios and enhancing the flexibility and rigor of the subsequent optimal configuration model (OCM). To improve the coordination of active–reactive FRs, a bi-level OCM with multi-timescale considerations is developed. Compared to traditional configuration methods, the proposed model not only improves economic efficiency but also ensures that system voltage remains within safe limits after configuration. The effectiveness and superiority of the proposed optimal configuration method are demonstrated through simulations on an improved 33-bus test system, where the model achieved a 9.208% reduction in annual cost compared to robust methods while maintaining voltage quality and avoiding overvoltage or equipment overloads. Full article

The advent of large-scale distributed generation (DG) has introduced several challenges to the voltage control of active distribution networks (ADNs). These challenges include the heterogeneity of control devices, the complexity of models, and their inherent fluctuations. To maintain ADN voltage stability more economically and quickly, a data-driven ADN voltage control scheme is proposed in this paper. Firstly, based on the multi-run state sensitivity matrix, buses with similar voltage responses are clustered, and critical buses are selected to downsize the scale of the model. Secondly, a linear voltage-to-power dynamics model in high-dimensional state space is trained based on the offline data of critical bus voltages, DGs, and energy storage system (ESS) outputs, utilizing the Koopman theory and the Extended Dynamic Mode Decomposition (EDMD) method. A linear model predictive voltage controller, which takes ADN stability and control cost into account, is also proposed. Finally, the effectiveness and applicability of the method are verified by applying it to an improved 33-bus ADN system. The proposed control method can respond more quickly and accurately to the voltage fluctuation problems caused by source-load disturbances and short-circuit faults. Full article

As the proportion of new energy access increases year by year, the resulting energy imbalance and voltage/trend distribution complexity of the distribution network system in the spatio-temporal dimension become more and more prominent. The joint introduction of electromagnetic rotary power flow controller (RPFC) and energy router (ER) can improve the high proportion of new active distribution network (ADN) consumption and power supply reliability from both spatial and temporal dimensions. To this end, the paper proposes an ADN expansion planning method considering RPFC and ER access. A two-layer planning model for RPFC and ER based on spatio-temporal characteristics is established, with the upper model being the siting and capacity-setting layer, which takes the investment and construction cost of RPFC and ER as the optimization objective, and the lower model being the optimal operation layer, which takes the lowest operating cost of the distribution network as the objective. The planning model is solved by a hybrid optimization algorithm with improved particle swarm and second-order cone planning. The proposed planning model and solving algorithm are validated with the IEEE33 node example, and the results show that the joint access of RPFC and ER can effectively improve the spatial-temporal distribution of voltage in the distribution network and has the lowest equivalent annual value investment and operation cost. Full article